Running title: INSERT RUNNING TITLE HERE
Munkhtsetseg^1^5\(^*\), Shimizu.
A^2, Matsuki. A^3, Batdorj. D^4, Matsui. H^1\(^*\)
\(^*\) To whom correspondence should
be addressed: e.munkhtsetseg@yahoo.com
1. Nagoya University, Japan
2. NIES
Abstract (150 words)
Storyline:
- A new pattern is emerged
- Air quality in urban sites is episodically dictated by dust events
in spring or late autumn, yet seasonally governed by anthropogenic
emissions in winter. [Air quality is governed by natural dust emission,
and anthropogenic emissions]
- With recent growing interest in urban life style, and combustion of
coal/oyutolgoi for heating winter conditions results a highly increase
in not only capital city but also towns
- In a result, spring coarse dust, plus winter fine pollutants
- spring coarse dust is immediately transported and deposited in the
source area, whereas winter fine pollutants is permanently stayed in the
source area due to stagnant atmosphere govern over entire country.,
perhaps floating in the near surface, deposits in the surface]
- Alarms, the Mongolian dust in the spring, optical properties might
be shifted; this gives … Gobi dust and sand storms has become tuiren,
from the shoroon shuurga. which clearly requires the attention.
- r ratio shows … emission source; dust might carry anthropogenic fine
particulates as well.
Introduction
* Advanced the knowledge of global dust, has reached to recognize the sources,.
- Classification dust brown color, seasonal characteristics, with coarse fractions.
- This knowledge further efficient to climate system when elaborating dust-aerosol effects.
- But, a large uncertainties in the global dust model has existed so for climate models which clearly limits our understanding the climate system and shape the facing global issues of global warming.
- This is mainly caused by the lack of parameterization and recognition of iterative changes controlled by the natural forces and anthropogenic drivings.
* Mongolian dust brown color, seasonal characteristics, with coarse fractions.
- Mongolian dust has an attention of the its mass fraction in global dust, yet unlikely elaborated in the climate models due to its majority of coarse fraction for its a small contribution to the climate system through its radiative feedback.
- But, such recognized characterization might get no longer valid due to recent change in the driving of the emissions of air particulate matters. A large high concentrations of PM2.5 in the capital city of Mongolia has been observed as a result of the heavy consumptions of coal as a winter heating has rapidly spread as a mining industry taken off since 2000. Winter weather stagnant conditions governed by the Siberian magnifies the concentrations of the particulate matter emissions by trapping the polluted air below the boundary layer, so that results in a very large high concentrations of PM2.5, locally. Even recognized as one of the highly polluted capital cities in the world.
- Therefore, It is important to examine the emerging changes and shifting patterns of air particulate matters in Mongolia. More importantly, it is essential to reveal the significant changes in the the altered fraction particularly, in the dust seasons considering its high potential of intriuging in the free atmosphere to transported in the long-distance, so carrying capacity of the role to shift the global climate system, and its side impacts on downwind regions.
- Study goal
- We hypothesize …
- Our study will benefit not only to the global dust research but also
climate, and further to the country itself for urban planning, and coal
combustion.
Research Qs
Therefore, we aimed to demonstrate the distinct temporal and spatial
variations of PM2.5 and PM10 across urban and rural Mongolia using
extensive data from 2008 to 2020.
On spring, the dust storm from the Gobi Desert contribute
significantly to increased aerosols in the atmosphere and ambient air
pollution, leading to sporadic peaks in PM10 concentrations reaching as
high as 64-234 \(\mu g m^{-3}\) per day
or exceeding 6000 \(\mu g m^{-3}\) per
hour (Jugder). concentrations of particulate matter is ephederemal, yet
vary depending on whether the pollution cause is natural or industrial,
local or transported, seasonal or non-seasonal, makes complex and
challenging. 1. Do concentrations of particulate matters differ in
between urban and rural sites, and even within Gobi sites? 2. Do
distinct temporal variations has existed among the sites? 3. Do PM2.5
particulates has contributed to the PM10 annual variations?
- If yes, how much, and when and where?
- What is the sd, mean, and median
- box plot
- violin
- scatter points, epidemic, sporadic
- Daily variations to examine it related to the heating
- 2 peaks: smaller and bigger
- compare the t-duration exceeds 50mug/m3/hour
4. Does it has distinct patterns among the sites regarding to the
drivings
- How PMs varies with the wind speed and visibility
- Do they differently explained with variables and changes in
drivings (with PCA analysis)
5. Is there any significant changes in time-series of PMs at 4
seasons
6. Is there any significant changes in ratio in the spring in
respect to winter?
The present study will contribute significantly to the understanding
of air particulate matter patterns in Mongolia and providing
comprehensive data insights for policymakers and public health
sectors. Our findings is useful not only for addressing national health
impacts but also beneficial for understanding air particulate matter
as ambient air pollution, and tackling atmospheric aerosol effects
in the climate system, and revealing their transboundary effects to
the downwind regions in South-east Asia.
Results
The spatio-temporal variations of the PMs at the study sites
To evaluate the spatial variations in particulate matter (PM)
concentrations, we displayed hourly observed values of PM10 and PM2.5
for all study sites (figure_3). The mean p-values indicate that PM
concentrations differ significantly at a 99% confidence level across all
sites (figure_3), with the exception of a 95% confidence level between
DZ and UB for PM10 (figure_3a), highlighting substantial concentration
disparities among sites. While quantitative differences in PM
concentration values exist across all sites, two key patterns emerge
when examining median deviations from mean values and irregular
observation fluctuations. For instance, PM10 demonstrates more erratic
behavior than PM2.5 at each location, particularly evident at ZU and SS
sites. Furthermore, the mean values calculated from hourly measurements
surpass the median concentrations for both PM10 and PM2.5 across all
sites, with notable prominence at UB and DZ locations. Consequently,
significant spatial differences in PM concentrations exist among all
sites, regardless of urban or rural classification. However, the sites
can be categorized into two groups based on their characteristics: UB
(urban) and DZ (rural town, Gobi); and SS (rural, Gobi) and ZU (rural,
Gobi). These findings for DZ appear to support our hypothesis of
emerging new emission patterns related to increased coal consumption
during winter months.
[AND] To examine the PM emerging patterns whether it related to
household heating fuel, we demonstrated annual variations in PM10 and
PM2.5 concentrations at the sites. [AND] Significant annual variations
in PM10 and PM2.5 levels demonstrating higher concentrations in colder
months and lower concentrations in warmer months at UB and DZ sites.
[AND] During colder months (January, November, December), PM10
concentrations contributed by elevated levels in PM2.5 were exceed 100
\(\mu g/m^3\), accompanied by PM10 and
PM2.5 concentrations reached their lowest points during warmer months
(May-September), with medians and ranges consistently below 50 \(\mu g/m^3\). This distinct seasonal trend,
peaking during the same cold months are supported by the diurnal
variations in PM10 and PM2.5 concentrations at sites DZ and UB. A
pronounced daily cycle is evident, at both DZ and UB sites, where PM
concentrations peak during nighttime and early morning hours
(approximately 8 PM to 4 AM UTC), with median values surpassing 50 \(\mu g/m^3\). PM2.5 concentrations
consistently follow a similar pattern but remain lower than PM10. In
contrast, both pollutants exhibit reduced concentrations during daytime
hours (8AM to 4PM UTC), likely due to increased atmospheric dispersion.
At site UB, a comparable daily trend is observed, but with lower overall
concentrations and less pronounced peaks. The variability, indicated by
the interquartile range, is higher during nighttime, suggesting the
influence of localized emission sources and reduced boundary layer
mixing. These diurnal patterns underscore the temporal dynamics of air
pollution, influenced by both anthropogenic activities and
meteorological conditions.
| high with violin meteorological effects |
Annual maximum in the winter for DZ and UB are from PM2.5, which
results an increase in PM10.
clearly states that …. DZ and UB are from the … household ., that
concentration can be as high as a arctic oscillation/Siberian high
intensifies with the heating, as low as it loose or may combination of
heating demand drops.
[BUT]
[THEREFORE] PM10 and PM2.5 concentrations are larger in the spring
followed by the autumn for SS and ZU sites. Annual PM10 and PM2.5
concentrations peaks in the winter aligned with the daily variations
happens at the heating time for SS and ZU sites. This points that the
increase in PM2.5 and PM10 is from the coal combustion. It requires the
cause the behind such the variations. DZ site is polluted in the winter
by the heating and in the spring by the natural dust.
- Do PM2.5 particulates has contributed to the PM10 annual variations?
Distinct temporal variations has existed among the sites. - PM2.5
particulates has contributed to the PM10 annual variations in UB and in
DZ in winter. - If yes, how much, and when and where? - What is the sd,
mean, and median - box plot - violin - scatter points, epidemic,
sporadic - Daily variations to examine it related to the heating - 2
peaks: smaller and bigger - compare the t-duration exceeds
50mug/m3/hour
The emission patterns of interrelations among meteorological
variables at the study sites
[AND] To distinguish the emission driving variables, at first we
demonstrated interrelations between wind speed, visibility among
particulate matters of PM10 and PM2.5 for each sites (figure_4).
Figure 6 illustrates the relationship between PM10 and PM2.5
concentrations across the four sites (UB, DZ, ZU, and SS) for different
seasons, alongside variations in wind speed (WS) and visibility (VIS).
At all sites, a strong positive correlation between PM10 and PM2.5 is
evident, as highlighted by the linear trendlines. The slopes suggest
proportionality, with PM2.5 typically contributing a significant
fraction of PM10 concentrations.
Seasonal differences are prominent, with higher PM concentrations
observed during November-February (marked as \("+"\) symbols), particularly at
UB and DZ, where values significantly exceed those from other periods.
Wind speed and visibility also exhibit notable interactions; higher PM
concentrations tend to correspond to lower wind speeds (smaller circles)
and reduced visibility (darker blue points). Conversely, during
March–June and July–October, marked by circles and triangles,
respectively, the overall concentrations are lower, especially at ZU and
SS, indicating better air quality conditions likely due to more
favorable meteorological factors.
The site DZ shows the largest variability in PM concentrations, with
extreme outliers during high-pollution periods. SS, on the other hand,
exhibits relatively low PM levels across all seasons, aligning with its
less polluted status. These results underscore the importance of local
factors, including meteorological conditions and emission sources, in
driving the observed PM dynamics.
DZ site is polluted in the winter by the heating and in the spring by
the natural dust.
- PMs varies with the wind speed and visibility
- In general, three distinct patterns were resulted with PCA analysis,
which is in consistent with temporal variation. explained with variables
and changes in drivings (with PCA analysis)
The recent trends in concentrations of PMS and fine-coarse
fractional changes at the sites
- There are significant changes in time-series of PMs at 4
seasons
- There any significant changes in ratio in the spring in
respect to winter in DZ.
- Close relationships was found between PM2.5 in winter and r
values in the spring.
Conclusions
- The spatio-temporal variations of the PMs at the study sites -
Concentrations of particulate matters differ in between urban and rural
sites, and even within Gobi sites. - Distinct temporal variations has
existed among the sites. - PM2.5 particulates has contributed to the
PM10 annual variations in UB and in DZ in winter. - If yes, how much,
and when and where? - What is the sd, mean, and median - box plot -
violin - scatter points, epidemic, sporadic - Daily variations to
examine it related to the heating - 2 peaks: smaller and bigger -
compare the t-duration exceeds 50mug/m3/hour
- The emission patterns of interrelations among meteorological
variables at the study sites
- PMs varies with the wind speed and visibility
- In general, three distinct patterns were resulted with PCA analysis,
which is in consistent with temporal variation. explained with variables
and changes in drivings (with PCA analysis)
- The recent trends in concentrations of PMS and fine-coarse
fractional changes at the sites
- There are significant changes in time-series of PMs at 4
seasons
- There any significant changes in ratio in the spring in respect to
winter PM2.5 in DZ.
- Close relationships was found between PM2.5 in winter and r values
in the spring. Thus, our research results clearly proves the distinct
variations in PMs has emerged. The dust fine-coarse fractions was
manifested at the town center for the Gobi sites, which reveals the that
Mongolian dust composites not only coarse dust, but also fine
particulate matters. The particulates likely consisted of the black
carbon, which may give a substantial effect on climate systems. if this
trend continues on as coal consumption with the population growth in the
future.
- CO Carbon monoxide is obtained due to incomplete combustion of
charcoal in a closed room.
- CO2
Results and Discussion
Distinct concentrations of coarse and fine
particulates among sites
- Compare the concentrations of PMs at UB is the 2. Significance level
difference 3. Conclude
Annual variations of $PM_{10}$ and
$PM_{2.5}$
- Clear annual variations at UB and DZ from pm2.5 pollutions 2. at ZU,
and SS has a seasonally peaks episodic spring and late autumn from
PM10
Daily variations of \(PM_{10}\) and \(PM_{2.5}\) at UB and DZ sites
Relationships between meteorological major
factors and variations of \(PM_{10}\)
and \(PM_{2.5}\)
Spatio-temporal distinct feature of variations
of \(PM_{10}\) and \(PM_{2.5}\) with PCA analysis
Patterns of meteorology and PMs at the 4
sites
Interannual and seasonal trends of \(PM_{10}\) and \(PM_{2.5}\) variations
Conclusions
In this study, we investigated the temporal variations of PM2.5 and
PM10 concentrations at the 4 sites of rural and urban those located
along the the wind corridor. Three distinct variations has been
detected.
A clear seasonal variations in the sites of UB and DZ is [Air quality
is governed by natural dust emission, and anthropogenic emissions] * Due
to rapid increase in urban, and combustion of coal/oyutolgoi for heating
winter conditions results a highly increase in not only capital city but
also towns * In a result, spring coarse dust, plus winter fine
pollutants [spring coarse dust is immediately transported and deposited
in the source area, whereas winter fine pollutants is permanently stayed
in the source area due to stagnant atmosphere govern over entire
country., perhaps float- ing in the near surface, deposits in the
surface] * Alarms, the Mongolian dust in the spring, optical properties
will be shifted; this gives … Gobi dust and sand storms has become
tuiren, from the shoroon shuurga. which clearly requires the
attention.
Following problems
- On downwind regions
- On national-level Demonstrating temporal and spatial variations of
air particulate matter has become important for understanding
characteristics of particulate matter in the climate system, providing
valuable information for well-established air quality measures, and
illustrating the good trace data for health studies. Because particulate
pollutants have a great impact on human health (Dockery and Pope,1994;
Harrison and Yin, 2000; Hong et al., 2002), high atmospheric
concentrations of these pollutants was a major concern particularly in
urban areas, in the last 2-3 decades. Recent studies highlight that even
low concentrations of these pollutants can lead to various health
issues, and may associate with morbidity and mortality across the life
span (Zigler et al., 2017). Children exposed to high levels of air
pollution show increased rates of asthma, decreased lung function
growth, and increased risk of early markers of cardiovascular disease
(Bourdrel et al., 2017; Gauderman et al., 2015; Hehua et al., 2017).
Short-term exposure with high level of PM10 resulted the chronic
cardiovascular disease in Mongolia (Enkhjargal 2020). In addition to
these health issues, (prenatal) neurodevelopmental impacts such as
effects on intelligence, attention, autism, and mood, while aging
populations experience accelerated cognitive decline when exposed to
high levels of pollution is detected (Power et al., 2016). Long-term
exposure to low levels of particulate matter, such as concentrations as
low as 10 \(\mu g m^{-3}\) (equilibrium
to WHO Air Quality Guidelines), has been linked to increased lung cancer
in the EU (Hvidtfeldt et al. 2021), with similar evidences reported in
Canada (Bai et al., 2019), and significantly higher rates captured in
China with concentrations up to 30 \(\mu g
m^{-3}\). Apparently, pollutants of particulate matters has
effects to various health issues with the different thresholds and
exposure durations. However, more in-depth and diversified research on
air pollution and its health effects is essential, with the detailed
information is necessary (Tan et al
- to have accuracy of assessing exposure to air pollution during
developmentally relevant time periods, such as trimesters or months
(Becerra et al., 2013; Gong et al., 2014; Kalkbrenner et al., 2014) or
weeks (Chiu et al., 2016). Many research findings/Numerous research
findings have advanced the field, and air quality indices is widely used
for providing guidance, and public perception of air quality has been
improved (Mirabelli et al., 2020).
Materials and Methods
Materials
Methods 3,000 words
Acknowledgements
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References (70)
Supplementary
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Materials and Methods
A description of study sites
According to the spatial magnitude of wind stress in Mongolia (Figure
1), the largest magnitude of wind speed is on the Gobi sites,
particularly those located in the southeast edge of the country.
- The impact of high winds on plant diversity varies across
environmental gradients of precipitation and soil fertility (Milchunas
et al., 1988).
- In the desert steppe zone, species richness was lower in the drier
years but did not vary with grazing pressure.
- In the steppe zone, species richness varied significantly with
grazing pressure but did not vary between years. Species richness is not
impacted by grazing gradient in desert steppe, but it is in the steppe
(Cheng et al., 2011).
In the last 2 decades, due to poverty and natural disasters there is
population immigration has taken place from the rural to urban,
especially to capital city of Mongolia. Due to tiny infrastructure to
provide the mega city with the dense population, it introduces the urban
pollution. Therefore, Ulaanbaatar air particulate matter mainly reflects
the coal burning, and partly, natural dust.
Consequently, the atmospheric environment and climate for Mongolian
Gobi has been impacted the most by frequent dust and and sand storm in
the spring.
Our study was carried out in Dalanzadgad (town center) (Tbl. 1;
43.57°N, 104.42°E), Sainshand (Tbl. 1; 44.87°N, 110.12°E) and Zamyn-Uud
(Tbl. 1; 43.72°N, 111.90°E) in the Gobi Desert, and at Ulaanbaatar
(Tbl.??.??°N, 104.42°E) (city center) located in the temperate Mongolian
steppe of Mongolia (Figure 2). Nomads and settlements of this sum have
raised a large number of livestock, and they rank at number 30 out of
329 sums for the largest number of livestock raised per sum (Saizen et
al., 2010). In the last decade, the number of dust events associated
with wind erodibility increased by 30 % in Bayan-Önjüül (Kurosaki et
al., 2011). This is an area where dust emissions activity has been
monitored on a long-term basis (Shinoda et al., 2010a) at a dust
observation site (DOS) adjacent to the study site (Fig. 1a). According
to long-term meteorological observations made at the monitoring station
of the Institute of Meteorology and Hydrology of Mongolia located near
the site, the prevailing wind direction is northwest. Mean annual
precipitation is 163 mm, and mean temperature is 0.1◦C for the period
1995 to 2005 (Shinoda et al., 2010b). Soil texture is dominated by sand
(98.1 %, with only 1.3 % clay and 0.6 % silt; Table 1; Shinoda et al.,
2010a). Insert figure legends with the first sentence in bold, for
example:
]
Table 1. A description of
datasets obtained at the sites
Scheme 1. Data handling procedure
Figure 2. Data gap filling
Figure 2b. Data gap filling
References